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In this paper we propose a novel method for object tracking in video images. The method is based on image segmentation and pattern matching. All moving and still objects in video images can be detected accurately with the help of efficient image segmentation techniques. We propose a hybrid algorithm for image segmentation using the notion of Particle Swarm Optimization (PSO) and Fuzzy-C-Means (FCM)...
A key challenge in real-world structural health monitoring (SHM) is diversity of damage phenomena and variability in environmental and operational conditions. Conventional learning techniques, while adequate for moderately complex inference tasks, can be limiting in highly complex and rapidly changing environments, especially when insufficient data is available. We present an adaptive learning methodology...
Suppose for a given classification or function approximation (FA) problem data are collected using sensors. From the output of the th sensor, features are extracted, thereby generating features, so for the task we have as input data along with their corresponding outputs or class labels . Here, we propose two connectionist schemes that can simultaneously select the useful sensors and learn the relation...
We propose an algorithm for the classification of structural damage based on the use of the continuous hidden Markov modeling (HMM) technique. Our approach employs HMMs to model time-frequency damage features extracted from structural data using the matching pursuit decomposition algorithm. We investigate modeling with continuous observation-density HMMs and discuss the trade-offs involved as compared...
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